Wednesday, June 18, 2025

Why Is Everything an Existential Crisis?

From WSJ:

https://www.wsj.com/opinion/why-is-everything-an-existential-crisis-mental-health-politics-meaning-5a469a24?mod=WTRN_pos7

Why Is Everything an Existential Crisis?

At the bottom of the vogue for this exotic term may be the basic human fear of death.

Andrew Hartz

June 16, 2025

So-called existential risks seem to be everywhere. Climate change, artificial intelligence, nuclear war, pandemics and more threaten to return us to nothingness. Most people using this term aren’t consciously evoking the philosophy of Sartre or Camus. Still, they may be drawing on associations with existentialism more than they realize and unconsciously expressing deeper concerns about morality and meaning.

In psychoanalysis, it isn’t unusual for a word to have an unconscious double meaning. For example, a patient in therapy might say that she can’t “bear” children. She could consciously mean that she’s unable to get pregnant, while also unconsciously communicating that she can’t stand children. Or a grieving patient who’s struggling to find the right word might say, “I’m at a loss.”

Similarly, the word “existential” points to catastrophic global threats, but it also echoes the concerns of existentialist philosophy, which addresses a range of topics from freedom to alienation. But the term is most strongly linked in the public imagination to issues of death and meaning. Saying an issue is “existential” can express that it’s deeply tied to one’s fear of dying and need for purpose.

We don’t have to overthink every word we say or hear, but there are signals that a word may have an unconscious double meaning. Therapists tend to think this phenomenon is most likely to occur when a word is used in an odd way—when it’s emotionally charged, when it’s repeated excessively or when it has clear resonance with one’s psychological concerns.

The recent political use of the word “existential” seems to check every box. People in the U.S. are shouting about how politics is “existential” at the same time that American society is suffering from a marked crisis of purpose. The term is strange and was used rarely in the past, but its use has been increasing as people have become less able to tolerate the risks and uncertainties of life.

Recent events—the pandemic, geopolitical conflicts, the increasing secularization of society—could have triggered Americans’ existential concerns. Perhaps other factors are at play as well. For many, social-media platforms intensify feelings of insignificance. A loneliness epidemic might heighten anxieties about meaning. Distrust of authority can lead to feelings of confusion and insecurity. Whatever the causes, the sense that everything is an existential crisis is likely exacerbated by the increasing emotional fragility of younger generations.

A prominent psychological theory, Terror Management Theory, posits that all people have anxiety about their mortality and that they cope with it in predictable ways. A robust body of evidence indicates that when people are reminded of their death, they try to boost their self-esteem, take steps to create a legacy and defend their worldview—be it secular or religious.

An extension of this theory is that people cope with anxiety about death by focusing their fears onto something more tangible, such as a current political cause. That cause can then become emotionally loaded with all of their anxiety and distress about mortality. This temporarily makes their anxiety feel more manageable, but it’s likely to contribute to fanaticism and emotional dysregulation around politics.

Political causes aimed at tackling “existential” risks are often associated with safety culture. People seem to have an unconscious hope that regulations could protect them from ever dying. Sometimes, these rules and rituals even take on an obsessive-compulsive quality, as if the repetitions will magically stave off unknown risks. Many Covid practices had this flavor.

Most clinicians think that fears of annihilation are psychologically primitive. Freud thought that these fears could stem from paranoia: “He hates me” becomes “they’re out to get me,” which devolves into “the world is going to be destroyed.”

Often, people come to yearn for an omnipotent state as the means to protect them from all their fears. They imagine total state control to protect them from all the internal feelings they can’t tolerate. Anxieties like this can easily become fodder for authoritarianism.

Politics won’t solve our deeper problems. Meaning and purpose have to be found on one’s own, and people must develop ways to manage the uncertainties and risks of life. This can be challenging, but it’s the only path toward fulfillment, wisdom and freedom—to say nothing of mental health.

Mr. Hartz is a clinical psychologist and the founder of the Open Therapy Institute

AI-Facilitated Intellectual Dark Age

This is along the lines of https://www.deseret.com/opinion/2025/06/17/ai-chatbots-therapists-children/

https://brownstone.org/articles/teachers-must-avert-an-ai-facilitated-intellectual-dark-age/

Teachers Must Avert an AI-Facilitated Intellectual Dark Age

David Thunder June 16, 2025 Education, Technology 4 minute read

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I remember watching a YouTube interview with a highly intelligent and observant entrepreneur, who cheerfully predicted that the time would come when AI programmes would replace teachers, rendering their jobs obsolete. The commentator in question was an enthusiastic advocate of personal and economic freedom and a vocal critic of the excessive incursions of State agencies in our personal lives. Yet for some reason, he seemed relatively unconcerned at the prospect of machines teaching our children.

Of course, there are tasks that most would happily relegate to AI programmes to the benefit of humanity, such as certain forms of tedious clerical work, a large chunk of manual labour, and the synthesis of unwieldy amounts of data. However, there are other tasks that cannot be delegated to a machine without endangering invaluable dimensions of our lives as human beings.

One of those tasks is teaching and learning, through which people learn to think, interpret the world, make rational arguments, assess evidence, make rational and holistic choices, and reflect on the meaning of their lives. For better or for worse, teachers, from kindergarten right up to university level, form the minds of the next generation. The formation of the mind relies on apprenticeship, imitation of a worthy model, and intellectual practice and training.

Much as an athlete fine-tunes his motor skills and muscle memory playing sport, and finds inspiration in an exemplary athlete, the student fine-tunes his mental skills thinking, reflecting, studying, analysing, and generating ideas and arguments, in dialogue with an inspiring teacher. There is both an interpersonal and “hands-on” dimension to human learning, both of which are indispensable.

Yet Artificial Intelligence is reaching the point where it has the capacity to automate and mechanise certain aspects of teaching and learning, marginalising crucial aspects of the learning process, most notably the way a teacher can model intellectual activity for the student, and the intellectual tasks a teacher assigns to students in order to fine-tune their mental skills and imagination. Many tasks which, just a few years ago, had to be undertaken “manually,” by which I mean, through the laborious activity, imagination, and effort of a human being, can now be performed automatically by AI.

When I wrote papers for my university degrees, I had to wade through texts, synthesise their content, and build an argument from scratch, using my own mind. Now, AI technology is tantalisingly close to being able to create a research paper from scratch, with a few prompts and sources provided by the user.

The end product, for example, a paper or reflection churned out by AI, may look very similar, or even largely identical, to the product of a non-AI-led writing process. But this “product” is generated largely by providing AI with the right prompts, not by working the creative and analytic muscles of the mind, or doing the mental “heavy-lifting” that is required in order to drill into a problem or take one’s intelligence or imagination to the next level.

This makes traditional teaching tools, such as the graded take-home paper, largely obsolete, because realistically, in a competitive environment, many students will not deprive themselves of the advantages of AI in the creation of graded work.

Even if a teacher encouraged or required students to write a paper without the assistance of AI, there is no reliable way to police such a requirement outside of the classroom, and it seems unfair for conscientious students to be outperformed by students of a more “pragmatic” bent who “milk” AI for all it’s worth.

This means that the whole teaching and learning process, including the evaluation of student work, will have to be re-conceived for a cohort of students increasingly comfortable using AI technologies. If teachers truly believe in the importance of a learning process that stretches and trains the intellectual abilities of the student and is not usurped at every turn by AI “shortcuts,” then they – we – will have to find new approaches to student assignments and evaluation.

These might include a greater emphasis on oral assessment, a shift to longer supervised technology-free exams, or un-graded writing assignments in which students might be more willing to forego the competitive advantage of AI if persuaded of the value of rising to an intellectual challenge.

There is a lot of concern expressed, understandably, about the prospects of mass unemployment as many tasks currently assigned to human beings get relegated to AI programmes. But we should not forget that one of the greatest risks of AI technology may be a degradation of the learning process itself, and thus a new intellectual dark age. It is up to teachers and teaching institutions to do all they can to avert such a catastrophic outcome.

Tuesday, June 17, 2025

Impact of charisma

Psychological impact of charisma.

Key excerpt:

In our hyperindividualized age, a lot of us are searching for a storyteller: someone or something to tell us what our lives mean. Compared with the sense of purpose and identity that past generations found in sturdy communities, now “it’s very difficult to tell the story of who you are and what you’re doing,” Dr. Kommers said. “Psychology and A.I. don’t have a way to help us with that. That’s one of the reasons there’s this pervasive feeling that technology doesn’t make our lives better.”
Americans are still waiting for a leader who invites us into a plotline that moves beyond 2025. We need a story that reckons with reality, but does not trap us in it.

https://www.nytimes.com/2025/06/16/opinion/charisma-history-trump.html

Charisma Rules the World

June 16, 2025

By Molly Worthen

Dr. Worthen, a historian at the University of North Carolina, Chapel Hill, is the author of “Spellbound: How Charisma Shaped American History From the Puritans to Donald Trump.”

The 2020s should have been the decade when American politics began to make sense. The multibillion-dollar industry of public opinion polling can turn vibe shifts into tweetable bar graphs and trend lines. Surveys have found that affiliation with traditional religious institutions has mostly declined over the past generation, so one might conclude that more Americans now form their worldviews and choose leaders based on cool logic and material interest. And over this data-driven landscape extends the lengthening shadow of our artificial intelligence overlords, who promise to rationalize more and more of our lives, for our own good.

Yet somehow, despite the experts’ interactive graphics and the tricks that large language models can do, it has only gotten harder to understand the worldviews and political choices of half the country (whichever half you don’t belong to). Perhaps, then, we should pay more attention to the human quirks that confound statisticians and that A.I. can’t quite crack — desires and drives that have not changed much over the centuries. That means rescuing a familiar word from decades of confusion and cliché: charisma.

In New Testament Greek, the word means gift of grace or supernatural power. But when we use it to describe the appeal of a politician, a preacher’s hold over his congregation or a YouTube guru with a surprisingly large following, we are taking a cue from the sociologist Max Weber. He spent much of his career studying what happens to spiritual impulses as a society becomes more secular and bureaucratic.

A little more than a century ago, he borrowed “charisma” from the Bible and Christian history to describe the relationship between leaders and followers in both religion and politics. Charisma, he wrote, is a form of authority that does not depend on institutional office, military might or claims on tradition. Instead, charisma derives from followers’ belief that their leader possesses a supernatural mission and power: “a certain quality of an individual personality by virtue of which he is set apart from ordinary men.”

Weber described himself as “religiously unmusical” and insisted that he was reinventing charisma in a “completely value-neutral sense.” But the magnetism that he observed in some leaders — and their followers’ sense of calling and duty — seemed to demand a spiritual description. The secular vocabulary developing in his corner of academia, the new disciplines of the social sciences, was not up to the task. “In order to do justice to their mission, the holders of charisma, the master as well as his disciples and followers, must stand outside the ties of this world,” he wrote.

Even as he resisted his colleagues’ tendencies to reduce human behavior to animal instincts and reflexes, Weber missed a key element. Charisma is not something that leaders have; it’s something that they do. Charisma is a kind of storytelling. It’s an ability to invite followers into a transcendent narrative about what their lives mean.

Charisma is not the same thing as charm or celebrity. Americans have mixed up these concepts since the 1950s and ’60s, when Weber’s idea leached into newspaper election coverage and everyday speech. When I began working on a book about charismatic leaders throughout American history, I was in this muddle myself. I confused charisma with charm: a person’s ability to engage you in conversation and make you feel like the center of attention.

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But winning elections, beginning a mass movement or starting a religion requires more than a knack for working a room. Charm, it turns out, is not even a uniquely human quality. The latest intelligent social agent chatbots like Replika have better social skills than many humans. Rob Brooks, an evolutionary biologist who set out recently to test A.I.’s ability to play on our social instincts, wrote that his Replika chatbot always wants to hear about his day, asks great follow-up questions and “really gets me.”

Anyone who has read or heard about Dale Carnegie’s “How to Win Friends and Influence People” knows that charm follows a formula (mainly, offer specific praise and focus on the other person’s problems rather than your own concerns). A.I. agents run these scripts better than we can. They “are really good at making you feel seen,” Dr. Brooks, who works at the University of New South Wales in Sydney, told me. With each generation of innovation, A.I. gets better at manipulating human “algorithms”: the impulses that we share with our fellow primates, especially our desire to like and be liked, just as chimpanzees groom one another to strengthen social bonds.

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But charisma may remain one of the few human dynamics that elude A.I., because it is not the conversational allogrooming at which A.I. excels. It is based not on primate instincts of attraction but on our aspirations to higher meaning, enlivened with a dose of the social friction that A.I. agents are designed to eliminate. Charisma can be just as repellent as it is attractive, so it usually baffles or disgusts anyone not under its thrall.

My research took me from Puritan mystics and early Mormons to Black nationalism and Pentecostal revivals to the cults and management gurus of the 1970s and ’80s, all the way to President Trump. Some of these figures possessed good looks, great oratorical skills, sex appeal or charm — but surprisingly few, as far as I could tell.

They had something far more important in common: They promised to pull back the veil on secret truth. They revealed how followers’ struggles have a purpose — one that the reigning elites and institutions belittled or missed entirely. In the 1630s, the Puritan midwife-theologian Anne Hutchinson recruited most of the Boston congregation with her promise that her fellow colonists could learn directly from scripture and the Holy Spirit that they were saved.

That divine assurance, she said, meant that they should not meekly accept everything their ministers taught. “I’ll bring you to a woman that preaches better Gospel than any of your black-coats that have been at the ninniversity,” one of her followers declared. When clergy and magistrates banished her from Massachusetts Bay in 1638, they might have expelled the heretic, but they couldn’t extinguish her idea’s core appeal: Americans’ hunger for a role in a cosmic story that turned their private anxieties into assurance that a higher power would, one day, remedy their worldly miseries and justify their suffering.

If leaders can tell stories like this, then they don’t need gravitas or charm. In fact their oratory and personal presence can — and almost certainly will — provoke mixed reviews. In the 1840s, one skeptic who met Joseph Smith, the founder of the Church of Jesus Christ of Latter-day Saints, described him as “a coarse, plebeian, sensual person in aspect, and his countenance exhibits a curious mixture of the knave and the clown. His hands are large and fat.”

Yet converts found an answer to their frustrations with traditional Christianity and personal dead ends in Smith’s story about the gold plates, his revelation of the young Republic’s central role in God’s plan and the divine mandate to build a new Zion. For them, just shaking his hand brought “the Holy Spirit in such great abundance that I felt it thrill my whole system, from the crown of my head to the soles of my feet,” one convert wrote.

These polarized reactions — based on whether the leader offers you a heroic role in his narrative, casts you as a villain or leaves you out entirely — are the real mark of charisma. For all the research on the importance of body language, whether we find a speaker bewitching or irritating depends on our relationship to the person’s message. Mr. Trump’s speeches strike his supporters as authentic truth telling in an era of fake news, while his critics struggle to understand how anyone has patience for his stream-of-consciousness rambling. “Some people would look at Trump and think, ‘That’s ludicrous. That’s not meaningful,’” Cody Kommers, a researcher at the Alan Turing Institute in London, told me. “But clearly for a lot of people, there is meaning being generated there, some sort of narrative that people are tapping into.”

Narratives usually follow patterns. In recent years, scholars have used software to analyze thousands of books and chart the basic emotional arcs of classic literature, ranging from the Icarus narrative of rise and fall to the triumphal Cinderella plot. Psychologists have found that when they interview people about their life stories with a series of questions based on the so-called hero’s journey — a narrative structure present in myths across cultures — they are more likely to find significance in the twists and turns of their own lives. Dr. Kommers and his colleagues have tried the same thing with large language models, or L.L.M.s. “We give them a character profile, give them the hero’s journey and have them tell the story again. The question is, can L.L.M.s show the same effects of restorying? Yes, they can.”

But charismatic leaders do far more than reproduce generic plot structures or parrot the stages of Joseph Campbell’s “Hero With a Thousand Faces.” They tell a strange story that speaks to the anxieties of their moment in history, not a formulaic one that could suit any time or place. They possess an uncanny intuition to zigzag when everyone else is heading straight.

Marcus Garvey supercharged the tradition of Black nationalism by reinventing — even breaking — older models of Black leadership. He arrived in New York City during World War I and honed a flair for pageantry that spoke to African American society of that place and time: full of veterans angry that the promises of Western democracy did not fully apply to them and members of Black fraternal orders who had found the dignity they deserved in uniforms and rituals.

At a time when most attention focused on Black leaders who were fair skinned and college educated, he cut a different figure. His archenemy, W.E.B. Du Bois, described Garvey as “a little fat black man” and a “demagogue.” But Du Bois belonged to the Black elite; Garvey’s working-class followers saw him through the empowering story he told. “If 400,000,000 Negroes can only get to know themselves, to know that in them is a sovereign power, is an authority that is absolute, then in the next 24 hours we would have a new race, we would have a new nation, an empire,” he preached.

Smartly dressed legions of his Universal Negro Improvement Association paraded behind their leader, who often wore a plumed helmet and purple, green and gold robes. Garvey’s vision for Pan-African economic, political and spiritual power was a remix of old and new archetypes: part Carnegie, part Napoleon, part Moses. Those who joined the association found themselves in three roles at once. They were self-made men and women, conquering soldiers and chosen people claiming a divine inheritance.

Charisma has no inherent moral valence. It can empower freedom fighters or enable tyrants. But in a healthy society, other authorities counterbalance the new picture of reality that a charismatic leader claims to reveal. Universities and government institutions staffed with experts, religious and philosophical traditions that place current events in a longer context, established media with professional journalists and international news bureaus — all these help ordinary people evaluate the story a leader is telling. But for more than a generation, study after study has documented the collapse of public trust in these institutions.

Our current moment encourages the impulse to retreat to your phone and ask a chatbot to help you sort out the world. It also invites a charismatic leader who can rework classic American story lines and answer the frustrations of people alienated from the invisible experts, confusing rules, profit motives and organizational inertia sometimes just called “the system.” (Weber described our modern condition as an “iron cage.”)

Mr. Trump began crafting a story to explain all this long before he formally entered politics. Since the 1980s, he has cast himself as the hero-entrepreneur who built an empire by working the system and the populist avenger who will vindicate the little guy. “I do get tired of seeing the country ripped off,” he told Oprah Winfrey in 1988, when she asked him whether he had considered running for president. “If it got so bad, I would never want to rule it out totally.”

Mr. Trump is the bard of the burn-it-down age. His mix of national and personal mythology fuses political strategy with his gut reactions. The more those reactions horrify his critics, the more his most committed supporters adore him, because you don’t have to be likable to be charismatic. Likability can be an impediment if you’re crusading to break through the polite lies of mainstream politics.

Let’s be clear about the human strangeness and genius of this. No machine programmed to spew out tropes according to statistical frequency could reproduce the components of Mr. Trump’s charisma or the appeal of Hutchinson, Smith and Garvey before him. Mr. Trump’s abrasive personal style and love of chaos are the opposite of a Replika chatbot’s soothing, frictionless responses. Yet both appeal in our secular, disconnected age, when many Americans choose to sit alone scrolling TikTok conspiracy theories instead of joining live human beings in a church, school board meeting, bowling league, Scout troop or any of the other depopulated relics of an earlier, more connected time.

In our hyperindividualized age, a lot of us are searching for a storyteller: someone or something to tell us what our lives mean. Compared with the sense of purpose and identity that past generations found in sturdy communities, now “it’s very difficult to tell the story of who you are and what you’re doing,” Dr. Kommers said. “Psychology and A.I. don’t have a way to help us with that. That’s one of the reasons there’s this pervasive feeling that technology doesn’t make our lives better.”

Americans are still waiting for a leader who invites us into a plotline that moves beyond 2025. We need a story that reckons with reality, but does not trap us in it.

_____

Molly Worthen, a professor of history at the University of North Carolina, Chapel Hill, is the author, most recently, of “Spellbound: How Charisma Shaped American History From the Puritans to Donald Trump.”

Wednesday, June 11, 2025

Diversity of thought

This isn't surprising, but it's interesting to see in graphic form.

https://x.com/JacobAShell/status/1932944065915416947

https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/bjso.12665

Attitude networks as intergroup realities: Using network-modelling to research attitude-identity relationships in polarized political contexts

Adrian Lüders, Dino Carpentras, Michael Quayle

First published: 11 July 2023

https://doi.org/10.1111/bjso.12665

Citations: 6

Abstract

We apply a newly developed attitude network-modelling technique (Response-Item Network, or ResIN) to study attitude–identity relationships in the context of hot–button issues that polarize the current US-American electorate. The properties of the network–method allow us to simultaneously depict differences in the structural organization of attitudes between groups and to explore the relevance of organized attitude–systems for group identity management. Individuals based on a sample of US-American crowd workers (N = 396) and the representative 2020 ANES data set (N = 8280), we model an attitude network with two conflictive partisan belief-systems. In the first step, we demonstrate that the structural properties of the attitude-network provide substantial information about latent partisan identities, thereby revealing which attitudes ‘belong’ to specific groups. In a second step, we evaluate the potential of attitudes to communicate identity-relevant information. Results from a vignette study suggest that people rely on their mental representations of attitude-identity links to structure and evaluate their social environment. By highlighting functional interdependences between (macro level) attitude structures and identity management, the presented findings help advancing the understanding of attitude-identity dynamics and socio-political cleavages.

INTRODUCTION

It is in the nature of academic disciplines to approach similar phenomena from different theoretical and methodological angles. In attempts to seek answers to pressing social challenges, however, boundaries between different scientific disciplines are blurring. The presented research aims to support this process and suggests a methodological approach that permits scholars to connect intraindividual representations of group membership with macro-level attitude-structures. We develop this research on two premises: the first premise states that coordinated attitude-structures, as exposed by network-modelling, can reveal and inform latent social identities hence making network-modelling a fruitful approach to study intergroup phenomena of interdisciplinary interest such as polarization. Two interdisciplinary research traditions lie at the core of this reasoning: A relational understanding of attitudes in which the meaning of one attitude is at least partially defined by its relation to other attitudes – an approach that has been advanced by sociologists interested in cultural schemas (Boutyline & Soter, 2021; DiMaggio, 1997; Goldberg, 2011) – and a social psychological understanding of attitudes as a substrate for shared selfhood (Bliuc et al., 2007; McGarty et al., 2009). To validate this premise, we model an attitude network of competing belief-systems based on a set of hot-button issues that polarize the current US-American electorate. The underlying computational process is inductively data driven which allows us to obtain a realistic image of the structural organization of the selected attitude set at the time of data collection across a sample. We spatially locate each individual participant in the network to connect the obtained macro level network properties with mechanisms relevant for group identity management. We demonstrate that participants' network position correlates strongly and significantly with self-reported levels of (a) partisan identification and (b) group-bias. Based on these findings, we conclude that the extracted attitude space can be understood as a snapshot of a social reality in which social identities are actively constructed and enacted.

A second premise deriving from this first set of findings is that because individuals hold internal representations of attitude-identity relationships, expressed attitudes should convey identity-relevant information about the person that holds them. Hogg and Smith (2007, p. 1) illustrate this aspect when describing attitudes as “windows on identity”. Quayle (2020) carries this point further by claiming that attitudes only become socially meaningful once they are expressed (using an analogy of a card game in which a card that is held on the hand only becomes meaningful when played). Simply put, here we claim that by knowing a person's opinion on certain issues, one should often be able to make a pretty good guess about that same persons' identity. With data from a vignette experiment, we demonstrate that learning a single attitude (e.g., one's standpoint towards abortion rights) allows people to estimate an interlocutor's partisan identity with striking accuracy. Additionally, we show that people not only use attitudes to categorize others as ingroup and outgroup members, but also to evaluate a person more or less favourably. Together, these two premises hold that attitudes are both the material from which the social structure is made, and the means for positioning oneself and others within it.

The presented findings are theoretically and practically meaningful. On a theoretical level, the results corroborate the idea that multiple attitudes are organized and represented in the form of socially meaningful belief-systems that can be depicted as networks. These belief-systems recursively and dynamically interact with symbolic representations of group identity (and are themselves a form of symbolic identity). These mutual dependencies should make attitudes socially functional as they allow people to exchange information about group identities and use this information for social judgement (Quayle, 2020). On a practical level, the results stress the importance of attitudes for interpersonal communication. While expressing and observing attitudes can help people to navigate and structure their social environment, these “advantages” are not without costs. Particularly in highly structured (i.e., polarized) opinion contexts, expressing an attitude that “trespasses” an outgroup belief-system may quickly lead to false categorization and misjudgement which may increases contextual pressures for individuals to “choose sides” and act in congruence with normative standards of the ingroup (Ton et al., 2023).

Attitude-identity relationships as bipartite networks

The concept of belief-systems as a constrained set of functionally related attitudes that informs and can be informed by (political) identities is not new (Converse, 2006). However, with increasing computational power, the possibilities for researchers to quantify these complex relationships have expanded as well. Computational network modelling provides researchers with methodological opportunities to reveal and study the structural organization of political belief-systems, for instance by documenting changes in belief-systems structures across societies (Boutyline & Vaisey, 2017; DellaPosta et al., 2015) or by studying functional dependencies between attitudes within a belief-system (Brandt & Sleegers, 2021). The present research adds to this growing literature branch and considers attitude networks from an intergroup angle. Drawing on the social identity approach (Reicher et al., 2010; Turner & Oakes, 1986), we conceptualize attitude networks as the overlaying reflection of an interactively and dynamically constructed intergroup reality that provides a specific set of affordances and demands to organize intergroup relations.

We follow the idea that attitudes, and the people who are holding them, can be depicted as a bipartite network (Quayle, 2020). In such a bipartite network, attitudes are related if they are jointly held by different people, hence forming a symbolic layer of socially meaningful belief-systems. People are connected if they are holding similar attitudes in common, thereby forming a layer of interconnected agents. A simple example of a bipartite network structure would be the following: in the current US-political landscape two socially plausible sets of opinions are (1) endorsing gun control and opposing abortion restrictions as well as (2) opposing gun control and endorsing abortion restrictions. Social plausibility thereby results from the fact that enough people are holding the two attitudes in either of the described ways. Now let us consider three individuals of which two are holding either one of the outlined attitudes combinations while a third person supports gun control and abortion regulation. In a bipartite network, the two individuals with the opposed attitude-sets would be unconnected with each other as they do not hold any attitude in common (they would be, however, fully connected if they would agree on both issues). On the other hand, the third individual who is holding an “implausible” set of opinions would be connected to each of the two former individuals via one attitude, respectively. In such a model, the social dynamics between those different individuals (e.g., categorizations, evaluations, influences) are a direct consequence of the attitudes that unite and divide them.

Applying this mind game to socio-political reality highlights its relevance for intergroup phenomena. Political scientists interested in the topic of partisan polarization suggested that the number of people who are holding cross-cutting attitudes is declining with the result that Democrats and Republicans are embracing increasingly exclusive and, hence, conflictive socio–political narratives (Abramowitz & Saunders, 2008; DellaPosta et al., 2015). Following the proposed social identity perspective on attitude-identity relationships, one can state that the normative understanding of partisan-based group membership is becoming increasingly interwoven with a specific set of issue positions that the members of each partisan group are expected to hold. In such highly structured attitude–identity systems, attitudes should serve as functional social markers that allow individuals to determine whether someone belongs to an ingroup or to an outgroup.

Attitudes as informative and functional social identity elements

Attitudes allow individuals to communicate aspects of who they are and to make inferences about the identity of others (Hogg & Smith, 2007; Lüders et al., 2022). Recent research by Dias and Lelkes (2021) offers empirical support for these ideas by showing that attitudes inform social judgements in an experimental research setting. Dias and Lelkes sought to disentangle effects of partisanship and issue disagreement on affective polarization and exposed participants to a series of vignettes, each transmitting manipulated information about a bogus persona that participants had to evaluate on a feeling-thermometer. The findings suggested that participants' judged others, not primarily based on the extent of actual political disagreement, but rather on a person's partisanship that was signalled by specific policy standpoints. In other words, participants seemed to use attitudes to make inferences about a person's group membership and judged them accordingly. Data reported by O'Reilly et al. (2022) suggests that attitudes do not only signal well-consolidated social identities (like political partisanship), but also provide a substrate for newly emerging social identities. Data from an online interaction experiment in which dyads learned their mutual agreement or disagreement on a set of neutral statements (e.g., “circle is a noble shape”) showed a significantly higher level of reported social identification between participants who agreed with each other as compared to participants who disagreed or who were grouped based on arbitrary criteria.

Advancing the understanding of attitude-identity relationships

The present research connects individual-level representations of attitude-identity links with macro-level attitude-structures (i.e., depicted as network), hence bridging two typically disconnected layers of analyses. Modelling attitude-structures as networks allows us to explore inter-attitude links through inductive computation, meaning that no “top-down” assumptions about the structural and distributional properties of attitudes are required. This approach provides us with more flexibility as compared to “standard” models that require psychometrically validated attitude-scales or experimental stimulus control to predict variation in an outcome of interest. Conversely, here we propose that group identities and some of their relevant mechanisms can be predicted from network characteristics. Specifically, we expect that the more structured or polarized an attitude network is, the more it should convey distinguishable (and potentially competing) group identities. The proposed links between attitudes and identities should hold direct implications for micro-level processes as we expect people to rely on their knowledge about attitude-identity links when they evaluate their social environment.

ResIN: a network-based approach to explore attitude-identity relationships

The Response-Item Network (ResIN, Carpentras et al., 2021) has been developed as a modelling technique for complex attitude systems. Like other belief network analyses, ResIN builds up a correlation network based on responses to a defined set of attitude items. However, it incorporates elements of Item-Response Theory which significantly enhances its flexibility. Specifically, ResIN treats each item-response as a single nominal variable (i.e., chosen vs. not chosen), hence breaking up the items' ordinal structure, which is then re-built under consideration of the full network. For instance, a survey that includes 10 Items, each answered on a 5-point scale, would result in a network of 50 interrelated nodes. These nodes would be organized based on correlations between item-responses (not based on correlations between single items as in conventional belief network analysis, e.g., Boutyline & Vaisey, 2017), thereby locating associated item-responses in relative spatial proximity. ResIN therefore allows the depiction of multiple belief-systems within a single network without supposing an underlying symmetry. These belief-systems may each comprise extreme, moderate, and central attitude responses from the underlying items, which provides researchers with a deeper understanding of the normative alignment of attitude of different “strengths.” Once a network space is built from the underlying attitude structure, single individuals may be located in the centre, at the periphery, or outside of a specific belief-system. To this aim, ResIN adds a spatial variable (reflected as an X-axis ranging from −1 to +1) that assigns a numeric value to each participant that indicates each participants specific position in the network. This final step is critical for the present research aims, as the obtained network-position score can be used as a predictor variable.

PRESENT RESEARCH

The present manuscript uses ResIN (Carpentras et al., 2022) to explore attitude-identity relationships in the context of the US-American electorate. In the first step, we model an attitude network based on participants' responses to a set of economical and socio-cultural issues. Since our primary aim is to demonstrate functional relationships between attitude structures and group identity mechanisms, we preselected issues known to reflect polarization of the current US-American electorate (Dinkelberget, O'Sullivan, al., 2021; Malka et al., 2014). Because of the polarizing nature of the selected issues, the corresponding attitude network should contain two distinguishable partisan belief-systems. Our first hypotheses correspond to the idea that attitudes are organized in form of socially meaningful belief-systems that provide both a substrate and a reflection of group identity. To evaluate this claim, we calculate a network-position score for each participant that we then use to predict symbolic and affective partisan identity elements. We pre-registered the following two formal hypotheses:

H1.The position of a participant within the obtained attitude network correlates significantly with self-reported partisan identification (i.e., as Democrat or Republican).

H2.The position of a participant within the obtained attitude network correlates significantly with self-reported group-bias (i.e., a relative preference for the ingroup over an outgroup).

To enhance the robustness of our findings, we repeat this analysis with representative data from the 2020 American National Election Survey (ANES). For our second set of hypotheses, we follow a quasi-experimental design to test the expected functionality of attitudes for social judgements. Specifically, we test whether attitudes can inform social categorization and affective evaluation processes. To this aim, we pre-registered the following two hypotheses:

H3.The extent to which participants categorize someone as an ingroup or outgroup member based on an observed attitude will be informed by that same attitude's network position.

H4.The relative distance between a participant's position in the network and the position of an observed attitude will significantly correlate with participants' affective evaluation of another person that is holding an observed attitude.

PARTICIPANTS AND MATERIAL

Ethics statement

The presented research received ethical approval from the ethical advisory board of the University of Limerick, Ireland.

Participants

We recruited a sample of N = 402 paid participants through the crowd working platform Prolific Academic. The sample size was determined by the available funds. Participants were eligible if they were (a) at least 18 years old, (b) US residents, (c) native English speakers, (d) in support of US Democrats, Republicans, or Independents, and (e) received at least 98% approval from previous surveys. We excluded six participants who did not pass an attention check at the beginning of the survey, leading to an effective sample size of N = 396. The gender distribution was 50.5% males, 48.7% females, and 0.8% non-binary persons. Most participants were White Americans (83.6%), followed by African Americans (7.8%), leaving 8.6% to other ethnicities. The mean age was 34 years (SD = 11.7; Range = 18–81). On a categorical scale, 58.1% self-identified as Democrats, 28% as Independents, and 13.9% as Republicans. Since the network analysis is trying to capture a sociometric property of society, we re-weighted each group using recommended weights (Gallup, 2021).1

Material

Participants were invited to take part in an online survey with a mean completion time of 8 min and 20 s. After providing informed consent, participants responded to a set of items that assessed political viewpoints and indicated their partisan identification.

Political attitudes

A set of eight political attitude items (Supporting information A.1 in Appendix S1) covered hot-button topics such as abortion, immigration, gun control, and gay marriage. Each item followed a 5-point scale format ranging from strong disagreement to strong agreement. All items were (re)coded so that disagreement referred to liberal positions and agreement to conservative positions (e.g., “abortion should be illegal;” “The federal government should make it more difficult to buy a gun” [reversed]).

Partisan identification

We used single items to measure partisan identification as Democrat, Republican, and Independent (e.g., “I identify with American Democrats”). Participants responded to each item on a 7-point scale with 1 indicating maximum disagreement and 7 indicating maximum agreement.

Group-bias

We asked participants to rate their feelings towards Democrats, Republicans, and Independents on a 100-point scale ranging from 1 = cold/unfavourable to 100 = warm/favourable. We calculated affective group-bias as relative group scores by subtracting Republican from Democrat evaluations (Druckman & Levendusky, 2019).

Vignette study

The second part of the survey followed a quasi-experimental protocol. We introduced this section to our participants with a short description of the upcoming task: “On each of the following pages you will see a person expressing a view on one of the political issues we asked you about earlier. Based on what you know about them, you will be asked to guess their political orientation and say how you feel about them.”

We randomly presented each participant eight attitude vignettes from a pool of 40. Each vignette expressed one of the five possible response-options for one of the eight political issues (Figure 1). Each vignette was followed by a set of questions that measured social categorization and social evaluation. To measure social categorization, we asked participants to evaluate on three items whether the person represented by the manikin on the vignette was a Democrat, a Republican, or an Independent. Each item followed a 100-point format, ranging from 1 = definitely not a [e.g., Democrat] to 100 = definitely a [e.g., Democrat]. A second item assessing affective social evaluation used the feeling thermometer described previously. We calculated relative group scores (i.e., Republican – Democrats) to operationalize social categorization and evaluation. Although we additionally ask participants to provide similar feedback about political Independents, we did not include this information into our analysis since it was not included into our pre-registration protocol.

Details are in the caption following the image — FIGURE 1
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Example of stimuli as used in the vignette study.

NETWORK MODELLING

To model the attitude network, we dummy-coded each scale position (i.e., response-option) in the original dataset. This resulted in a new dataset in which each column reflected a different item (e.g., gun control:strongly agree) and each row a different participant. This new dataset contained only ones and zeros: one if a participant selected the response-option of a column; and zero if a participant did not select the response-option of a column. Since each of the eight items followed a 5-point format, the resulting dataset comprised 40 columns (8 items × 5 response-options = 40) corresponding to 40 network nodes. To estimate links between two selected attitudes, we calculated phi correlation coefficients (Guilford, 1941) with the following formula:

ϕ (i, j) = \frac{n_{11} n_{00} - n_{10} n_{01}}{\sqrt{n_{1 ●} n_{0 ●} n_{● 1} n_{● 0}}}

The letters i and j represent two different response options (which correspond to two columns in the dataset and two nodes in the network). The symbol $n_{yz}$ represents the number of rows in which the first column is equal y, and the second column is equal z. For example, $n_{11}$ is the number of rows in which both columns equal one (i.e., the number of participants who selected both response options). Conversely, $n_{00}$ is the number of rows in which both columns equal zero (i.e., the total number of participants who selected neither of the two response options). When one of the two entries is marked with a dot, such as in $n_{y ●}$ a variable may be either one or zero. Therefore, $n_{0 ●}$ reflects the number of participants who did not select the first response option but may or may not have selected the second response option. Finally, $n_{● 0}$ reflects the number of participants who may or may not have selected the first response option, but not the second. We did not calculate this value for response options that belong to the same item as they would be mutually exclusive (i.e., participants could not mildly and strongly agree with a single item).

To build up the attitude network, we estimated the cartesian position of each attitude position via the Networkx force-directed positioning algorithm (Hagberg et al., 2008) in Python. The algorithm treats edges as springs (holding nodes close) whereas the nodes themselves are treated as repelling objects. Attitudes that participants frequently selected together are therefore located in relative proximity in the network whereas attitudes that rarely co-occurred in a single participants' responses are placed further apart.

RESULTS

Figure 2a depicts the extracted attitude network. A visual inspection of the network reveals two attitude clusters. To understand whether partisanship was a latent factor overlaying the two clusters, we generated a heatmap by correlating the selection of each node with participants' self-reported partisan identification.2 As shown in Figure 2b, the cluster reflecting the Democrat belief-system almost exclusively contained extreme attitudes as indicated by strong disagreement with each of the eight items. Conversely, the cluster reflecting the Republican belief-system contained a wider range of attitude responses ranging from mild disagreement to maximum agreement. Note that these nuances would remain undetected by methods that consider Likert-type items as intervals or use arbitrary cut-offs. Supporting information B in Appendix S1 provides an overview of the specific issue positions that correspond to each cluster.

Extracted attitude network. (a) A visual representation of the extracted attitude network revealing a distribution of forty attitudes into two clusters. Note: Dark Blue = Strong Disagreement; Pale Blue = Moderate Disagreement; Grey = Neutral; Orange = Moderate Agreement; Red = Strong Agreement. (b) Two attitude clusters depicting latent Democrat (blue) and Republican (red) belief-systems.

Step 2: Confirmatory analysis

To test our first set of hypotheses, we located each participant within the attitude network. To this end, we calculated each participant's network position scores by averaging the location of all nodes corresponding to a participant's responses to the eight political issues. Since the network's spatiality followed the logic of an underlying X-axis that ranges from −1 (left side) to +1 (ride side), participants' network position scores fall within this spectrum. For instance, a participant who strongly disagrees with all eight items, would obtain a strong negative value (close to −1) reflecting a position in the Democrat cluster. Conversely, a participant who strongly agrees with half of the items and is neutral towards the other half, would obtain a positive value (between 0 and 1) and hence be located near to the Republican cluster (Figure 3).

We tested our first hypothesis by correlating participants' network positions with their relative identification as Democrat or Republican. The results supported our prediction, indicating strong and significant associations between participants' network position and self-reported partisanship, r = .72, p < .001. The more precisely participants were located within one of the two clusters (see Figure 3 for a visual example), the more they identified as a Democrat or as a Republican. Following the same procedure, we correlated participants' network position with self-reported group-bias to test our second hypothesis. Again, the results revealed strong and significant relationships between the two variables, r = .73, p < .001. The results therefore support our first set of hypotheses suggesting that the obtained attitude structures overlap with subjectively experienced social identities.

Robustness check

To validate the robustness of the obtained findings, we replicated our analyses based on the 2020 ANES dataset (ANES, 2021). The dataset is particularly well suited for our research aims as it includes items similar or equal to those that we used in our own survey (Supporting information A.2 in Appendix S1). The 2020 ANES dataset includes responses from 8280 participants representing the national adult US-American society. The survey was conducted in two waves (i.e., before and after the 2020 presidential election). For our analysis we used mainly the first wave (two items, however, were only available in the second wave). Replicating the described procedure, we modelled an attitude network based on the correlations that underlie participants´ item responses to eight political issues. The obtained network (Figure 4) was comparable to the one we obtained based on our convenience sample with a tighter Democrat cluster of mainly extreme attitudes and a looser Republican cluster with moderate to extreme viewpoints. We calculated participants' network position and correlated the obtained values with partisan identification (H1) and group-bias (H2). The resulting correlation coefficients mirrored those obtained with our convenience sample with r = .73, p < .001 for partisan identification and r = .79, p < .001 for group-bias.

Vignette study

The previous findings suggest that network characteristics can inform intrapsychological perceptions of group identity, hence supporting a bipartite model in which attitudes are linked if commonly held by people, while people are linked through the attitudes that they share.3 We next tested whether the structural characteristics of attitudes as reflected in the obtained network could also predict social judgements. Focusing firstly on socio-cognitive identity mechanisms (H3), we correlated participants' categorization of others as Democrats and Republicans with the position of an observed attitude in the network. The results revealed a strong, positive correlation, r = .90, p < .001, suggesting that the obtained attitude network overlapped largely with participants' social representation of whether a specific attitude "belongs" to a Democrat or Republican belief-set. Finally, we tested the prediction that observed attitude differences can likewise inform affective judgements, hypothesizing that larger attitude divergence should lead to more negative social evaluations (H4). We correlated participants' own network position with their submitted evaluation scores (Figure 5). The results suggested a moderate significant correlation, r = .49, p < .001, thus corroborating our fourth and last hypothesis.

DISCUSSION

The present research used ResIN (Carpentras et al., 2022) to model an attitude network based on participants' responses to eight political issues that are currently dividing Democrats and Republicans. Going beyond existing belief network methods (Boutyline & Vaisey, 2017; Brandt & Sleegers, 2021), this approach enabled us to observe partisan belief-systems without assuming an underlying symmetry. Furthermore, unlike methods such as hierarchical clustering (Murtagh & Contreras, 2012), ResIN does not make binary classifications of participants into one group or the other. Instead, people can hold attitudes of different groups and be located on the periphery of one, or in-between different belief-systems. In that same way, ResIN also differs from other network approaches that studied attitude-identity links on the intergroup level by clustering survey respondents into like-minded “communities” (therefore treating nodes as people and edges as connections between two people) (Dinkelberg, O'Reilly, et al., 2021; Maher et al., 2020). In Supporting information E: Appendix S1, we also compare our findings with results obtained from a more conventional procedure that relies on composite mean scores from participants' survey responses rather than on network-position scores. Whereas both approaches produced similar results for H1–H3, ResIN outperformed the conventional approach for H4 in which participants evaluated others based on an observed attitude. The proximity of the results from the two approaches, however, should not conceal the fact that the two attitude clusters that corresponded to competing partisan belief-sets were completely inductively obtained. This is a very different approach to the common practice of averaging items into scales, which not only permitted us to include items without having strong a priori assumptions about their interrelatedness, but, as we will outline in the further discussion, also avoids abandoning a lot of useful information about an items' ordinal structure and its nuanced meaning for particular identities.

Capitalizing on the described methodological advantages, we demonstrated the potential of ResIN to advance the understanding of attitude-identity relationships on the intergroup level. Our theorizing was informed by the social identity approach (Reicher et al., 2010) which proposes intragroup synchronization and outgroup contrasting as important identity mechanisms, and, more specifically, by a bipartite network model of attitude-identity relationships (Quayle, 2020) in which socially shared attitude combinations form socially meaningful belief-systems that differentiate people into groups based on attitude (dis)agreement. In the first step, we correlated participants' network position with self-reported levels of partisan identification and group-bias to demonstrate a functional dependency between macro-level attitude structures and intrapsychological self-regulation. The obtained results indicated strong and significant relationships of r > .70 in a convenience as well as in a representative sample, thus corroborating our first hypotheses. The closer participants were located to one of the two belief-systems, the more they identified as partisan members and the more biased was their emotional representation in favour of their ingroup.

Our first set of findings suggests that attitudes, emotions, and symbolic self-descriptions are interconnected elements which together define the meaning and the experience of partisan-based group identity. Such a holistic view on partisanship challenges interpretations of polarization as a largely emotional phenomenon (c.f., Iyengar et al., 2012). The results also show that the embedding of attitude positions into partisan identities is highly asymmetrical and thus more complex than bivariate polarization models would predict. Not only does the presented data suggest that Democrats embrace more extreme viewpoints on the selected issues compared with Republicans, but also that the Republican cluster includes some surprising issue positions that (under interval assumptions) might be assumed to fall into the Democrat cluster (c.f., Figure 2a). These differences may hold important practical implications. Starting with an optimistic interpretation, group cleavages might be easier to overcome based on issues in which identities are not tied to a specific position. For instance, the present data suggests that normatively acceptable viewpoints for Republicans on gay marriage, abortion rights, and environmental protection through business regulation range from mild agreement to extreme disagreement, hence, providing a potential space for political negotiation (c.f., Supporting Information B in Appendix S1). A pessimistic interpretation, however, would be that because neutral issue positions are largely embedded into the Republican belief-system (rather than being equally distributed between Republicans and Democrats), they may get “pulled over” to the Republican extreme. A similar dynamic has been suggested by previous research on vaccine hesitancy where the isolation of pro-vaccine attitudes (i.e., reflected by long edges between pro-vaccination and neutral attitudes and short edges between neutral and anti-vaccination attitudes) were associated with lower vaccination coverage in the following year (Carpentras et al., 2022). Returning to the present context, such dynamics would increase bipartisan polarization due to a gradually disappearing centre.

The vignette study in the second part of our research tested another claim of the bipartite model, namely that attitudes (due to their alignment with identities, as demonstrated in H1 and H2) carry identity-relevant information hence making them functional tools for social judgement. The results showed that participants were able to categorize a person as Democrat or Republican based on a single attitude with remarkable accuracy (reflected by a correlation index of r = .90). In other words, participants were seemingly well aware of the organization of Democrat and Republican belief-sets. Participants also showed noticeable differences in their evaluation of others depending on the relative distance between their own position and the position of the other's attitude in the network (r = .49). Findings by Dias and Lelkes (2021) suggest that attitudes signal partisan identities and therefore drive social judgements. We perfectly agree with this conclusion. One aspect that is important to emphasize, however, is that the present approach did not force us to define a priori which attitude would correspond to which group. The advantage of this gain in flexibility is that we can explore the degree of “tolerance” for different attitude positions in different groups. According to the present findings, Democrats (more than Republicans) tightly centre their belief-system around a set of positions at the extremes of these particular items, implying that people who deviate from these positions are likely to be considered as outgroup members (extremity should thereby be understood as a function of both, the formulation of the item and the response). It is possible that holding extreme (and thus unnegotiable) attitudes on important social-political issues has become increasingly identity defining for Democrats, not least in response to Donald Trump's controversial presidency. The pattern does not imply that Republicans are more tolerant than Democrats, nor that Republicans could deal better with attitudinal uncertainty. It does imply, however, that –at this particular moment in time– Democrats and Republicans are constructing and managing their partisan identities differently in relation to the topics reflected in these questionnaire items. Research suggests that social category membership (e.g., being White, Christian) is more important for the construction of Republican identity than it is for Democrat identity (Mason & Wronski, 2018). Fulfilling such normative criteria may hence qualify someone as a valid group member even if that same person may hold somewhat liberal views on, for example, gay marriage.

Ultimately, the association between identity and its material (e.g., attitudes; clothing; manners; language; food preferences; morality etc.) are dynamically and actively constructed through the activities and attributes of group members and group leaders. The aim of this manuscript was to introduce ResIN as a network-modelling approach that may help research to further explore the dynamic interplay between attitudes and identities and their functional expressions in intergroup contexts. Here, we have focused on well-established political partisan identities and on a set of attitudes strongly embedded into these identities to provide a comprehensible overview of the suggested approach. An obvious future step will be to extend the method to visualize intersecting identities. For example, Republican women may be more ambivalent about abortion than Republican men; perhaps along with highly religious Democrats. Additionally, future research applying the method may exploit its potential in contexts of newly emerging ideological narratives and group identities that feed on them. We believe that the insights obtained from such efforts would have important implications for a range of pressing social phenomena, including, among others, the politicization of lifestyle behaviour, the formation of social movements, and polarization in light of current and future challenges.

AUTHOR CONTRIBUTIONS

Adrian Lüders: Conceptualization; data curation; investigation; methodology; project administration; writing – original draft. Dino Carpentras: Conceptualization; formal analysis; methodology; visualization; writing – review and editing. Michael Quayle: Conceptualization; funding acquisition; supervision; writing – review and editing.

ACKNOWLEDGEMENTS

This work was supported by the European Union's Horizon 2020 research and innovation program: European Research Council (Grant No. 802421) & Marie Skłodowska-Curie (Grant No. 891347). Open access funding provided by IReL.

CONFLICT OF INTEREST STATEMENT

None to declare.

LDS psychology of belief

Wednesday, June 18, 2025